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A vector heterogeneous autoregressive index model for realized volatility measures

Gianluca Cubadda (), Barbara Guardabascio () and Alain Hecq ()

International Journal of Forecasting, 2017, vol. 33, issue 2, 337-344

Abstract: This paper introduces a new model for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The vector heterogeneous autoregressive index model has the property of generating a common index that preserves the same temporal cascade structure as in the HAR model, a feature that is not shared by other aggregation methods (e.g., principal components). The parameters of this model can be estimated easily by a proper switching algorithm that increases the Gaussian likelihood at each step. We illustrate our approach using an empirical analysis that aims to combine several realized volatility measures of the same equity index for three different markets.

Keywords: Common volatility; HAR models; Index models; Combinations of realized volatilities; Forecasting (search for similar items in EconPapers)
Date: 2017
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Related works:
Working Paper: A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures (2016) Downloads
Working Paper: A Vector Heterogeneous Autoregressive Index model for realized volatility measures (2015) Downloads
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Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:337-344